Comparative Evaluation of Three Schaake Shuffle Schemes in Post-processing GEFS Precipitation Ensemble Forecasts

Comparative Evaluation of Three Schaake Shuffle Schemes in Post-processing GEFS Precipitation... AbstractNatural weather systems possess certain spatiotemporal variability and correlations. Preserving these spatiotemporal properties is a significant challenge in post-processing ensemble weather forecasts. To address this challenge, several rank-based methods, the Schaake Shuffle and its variants, have been developed in recent years. This paper presents an extensive assessment of the Schaake Shuffle and its two variants. These schemes differ in how the reference multivariate rank structure is established. The first scheme (SS-CLM), an implementation of the original Schaake Shuffle method, relies on climatological observations to construct rank structures. The second scheme (SS-ANA) utilizes precipitation event analogs obtained from a historical archive of observations. The third scheme (SS-ENS) employs ensemble members from the Global Ensemble Forecast System (GEFS). Each of the three schemes is applied to post-process precipitation ensemble forecasts from the GEFS for its first three forecast days over the Mid-Atlantic region of the U.S. In general, the effectiveness of these schemes depends on several factors including the season (or precipitation pattern) and the level of grid-cell aggregation. It is found that (1) the SS-CLM and SS-ANA behave similarly in spatial and temporal correlations; (2) by a measure for capturing spatial variability, the SS-ENS outperforms the SS-ANA, which in turn outperforms the SS-CLM; and (3), overall, the SS-ANA performs better than the SS-CLM. The study also reveals that it is important to choose a proper size for the post-processed ensembles in order to capture extreme precipitation events. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Hydrometeorology American Meteorological Society

Comparative Evaluation of Three Schaake Shuffle Schemes in Post-processing GEFS Precipitation Ensemble Forecasts

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Publisher
American Meteorological Society
Copyright
Copyright © American Meteorological Society
ISSN
1525-7541
D.O.I.
10.1175/JHM-D-17-0054.1
Publisher site
See Article on Publisher Site

Abstract

AbstractNatural weather systems possess certain spatiotemporal variability and correlations. Preserving these spatiotemporal properties is a significant challenge in post-processing ensemble weather forecasts. To address this challenge, several rank-based methods, the Schaake Shuffle and its variants, have been developed in recent years. This paper presents an extensive assessment of the Schaake Shuffle and its two variants. These schemes differ in how the reference multivariate rank structure is established. The first scheme (SS-CLM), an implementation of the original Schaake Shuffle method, relies on climatological observations to construct rank structures. The second scheme (SS-ANA) utilizes precipitation event analogs obtained from a historical archive of observations. The third scheme (SS-ENS) employs ensemble members from the Global Ensemble Forecast System (GEFS). Each of the three schemes is applied to post-process precipitation ensemble forecasts from the GEFS for its first three forecast days over the Mid-Atlantic region of the U.S. In general, the effectiveness of these schemes depends on several factors including the season (or precipitation pattern) and the level of grid-cell aggregation. It is found that (1) the SS-CLM and SS-ANA behave similarly in spatial and temporal correlations; (2) by a measure for capturing spatial variability, the SS-ENS outperforms the SS-ANA, which in turn outperforms the SS-CLM; and (3), overall, the SS-ANA performs better than the SS-CLM. The study also reveals that it is important to choose a proper size for the post-processed ensembles in order to capture extreme precipitation events.

Journal

Journal of HydrometeorologyAmerican Meteorological Society

Published: Oct 26, 2017

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